{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uFALlc_i18Nf"
      },
      "source": [
        "### Copyright 2023 Google LLC.\n",
        "\n",
        "### Licensed under the Apache License, Version 2.0 (the \"License\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PNeKa6S7WkPY"
      },
      "source": [
        "### This colab contains a TensorFlow implementation and a demo of the learning rate scheduler proposed in the paper \"Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets\".\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P2p7iJAAWocW"
      },
      "source": [
        "# Learning Rate Scheduler Implementation\n",
        "\n",
        "Below is a TensorFlow implementation of the scheduler."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "EjpGSpj9PI3-"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "def lr_scheduler(loss, ideal_loss, x_min, x_max, h_min=0.1, f_max=2.0):\n",
        "  \"\"\"Gap-aware Learning Rate Scheduler for Adversarial Networks.\n",
        "\n",
        "    The scheduler changes the learning rate of the discriminator (D) during \n",
        "    training in an attempt to keep D's current loss close to that of D's ideal\n",
        "    loss, i.e., D's loss when the distribution of the generated data matches\n",
        "    that of the real data. The scheduler is called at every training step.\n",
        "    See the paper for more details.\n",
        "\n",
        "    Let x := abs(loss-ideal_loss) be the optimality gap. The scheduling function\n",
        "    s(x) is a piecewise function defined as follows:\n",
        "        f(x) := min(f_max^(x/x_max), f_max)  if loss \u003e= ideal_loss\n",
        "        h(x) := max(h_min^(x/x_min), h_min)  if loss \u003c ideal_loss.\n",
        "\n",
        "    When loss \u003e= ideal_loss, the scheduler increases the learning rate in\n",
        "    proportion to the optimality gap x. At x=x_max, the scheduler reaches its\n",
        "    maximum value f_max. Similarly, when loss \u003c ideal_loss, the scheduler\n",
        "    decreases the learning rate. At x=x_min, it reaches the minimum allowed\n",
        "    value h_min.\n",
        "\n",
        "    Note: This function outputs s(x), which can be used to multiply the (base)\n",
        "    learning rate of the optimizer.\n",
        "\n",
        "    Args:\n",
        "      loss: the loss of the discriminator D on the training data. In the paper,\n",
        "        we estimate this quantity by using an exponential moving average of\n",
        "        the loss of D over all batches seen so far (see the section \"Example\n",
        "        Usage for Training a GAN\" in this colab for an example of the\n",
        "        exponential moving average).\n",
        "      ideal_loss: the ideal loss of D. See Table 1 in our paper for the\n",
        "        ideal loss of common GAN loss functions.\n",
        "      x_min: the value of x at which the scheduler achieves its minimum allowed\n",
        "        value h_min. Specifically, when loss \u003c ideal_loss, the scheduler\n",
        "        gradually decreases the LR (as long as x \u003c x_min). For x \u003e= x_min, the\n",
        "        scheduler's output is capped to the minimum allowed value h_min. In the\n",
        "        paper we set this to 0.1*ideal_loss.\n",
        "      x_max: the value of x at which the scheduler achieves its maximum allowed\n",
        "        value f_max. Specifically, when loss \u003e= ideal_loss, the scheduler\n",
        "        gradually increases the LR (as long as x \u003c x_max). For x \u003e= x_max, the\n",
        "        scheduler's output is capped to the maximum allowed value f_max. In the\n",
        "        paper we set this to 0.1*ideal_loss.\n",
        "      h_min: a scalar in (0, 1] denoting the minimum allowed value of the\n",
        "        scheduling function. In the paper we used h_min=0.1.\n",
        "      f_max: a scalar (\u003e= 1) denoting the maximum allowed value of the \n",
        "        scheduling function. In the paper we used h_max=2.0.\n",
        "\n",
        "    Returns:\n",
        "      A scalar in [h_min, f_max], which can be used as a multiplier for the\n",
        "        learning rate of D.\n",
        "  \"\"\"\n",
        "\n",
        "  x = tf.math.abs(loss-ideal_loss)\n",
        "  f_x = tf.clip_by_value(tf.math.pow(f_max, x/x_max), 1.0, f_max)\n",
        "  h_x = tf.clip_by_value(tf.math.pow(h_min, x/x_min), h_min, 1.0)\n",
        "  return tf.cond(loss \u003e ideal_loss, lambda: f_x, lambda: h_x)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xvlrwgzeYwPb"
      },
      "source": [
        "# Example Usage for Training a GAN\n",
        "\n",
        "Below we demonstrate how to use the scheduler for training a GAN on MNIST. We use a DCGAN architecture and the standard non-saturating GAN loss (based on cross-entropy). The ideal loss for this GAN is log(4) (see Table 1 in the paper for more details).\n",
        "\n",
        "The scheduler is invoked in the last cell, titled \"Training Loop\".\n",
        "\n",
        "Note: The architecture and code below are adapted from the official TensorFlow tutorial on DCGAN (https://www.tensorflow.org/tutorials/generative/dcgan)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jAfZuG73WlzH"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z9Q7PZJce7lS"
      },
      "outputs": [],
      "source": [
        "from math import log\n",
        "from dataclasses import dataclass\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "layers = tf.keras.layers\n",
        "\n",
        "np.random.seed(1)\n",
        "tf.random.set_seed(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aVTj6D0wU3ZQ"
      },
      "source": [
        "## Define Hyperparams\n",
        "\n",
        "Set the hyperparams of the GAN, optimizer, and the scheduler."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ge26RqT-U14k"
      },
      "outputs": [],
      "source": [
        "@dataclass\n",
        "class TrainingParams:\n",
        "  # If true, turn on the learning rate scheduler.\n",
        "  use_lr_sched: bool = True\n",
        "  # Base learning rate for Adam.\n",
        "  lr: float = 1e-3\n",
        "  # Parameter beta_1 for Adam.\n",
        "  beta_1: float = 0.5\n",
        "  num_epochs: int = 50\n",
        "  batch_size: int = 256\n",
        "  # Dimension of the noise vector used by the generator.\n",
        "  noise_dim: int = 128\n",
        "\n",
        "@dataclass\n",
        "class SchedulerParams:\n",
        "  # Since we're using a non-saturating GAN, the ideal loss is log(4).\n",
        "  ideal_loss: float = log(4)\n",
        "  x_min: float = 0.1*log(4)\n",
        "  x_max: float = 0.1*log(4)\n",
        "  h_min: float = 0.1\n",
        "  f_max: float = 2.0\n",
        "\n",
        "\n",
        "training_params = TrainingParams()\n",
        "scheduler_params = SchedulerParams()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CWDUMS32UnnZ"
      },
      "source": [
        "## Load Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "executionInfo": {
          "elapsed": 3153,
          "status": "ok",
          "timestamp": 1674598197297,
          "user": {
            "displayName": "Hussein Hazimeh",
            "userId": "12699877532746750614"
          },
          "user_tz": 300
        },
        "id": "GNM_Jmc3ZkuH",
        "outputId": "fbfa3c7b-b0cb-4209-aea2-6491ba7d934d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "def load_data():\n",
        "  (train_images, _), (test_images, _) = tf.keras.datasets.mnist.load_data()\n",
        "  train_images = train_images.reshape(train_images.shape[0], 28, 28,\n",
        "                                      1).astype('float32')\n",
        "  train_images = (train_images - 127.5) / 127.5\n",
        "  test_images = test_images.reshape(test_images.shape[0], 28, 28,\n",
        "                                    1).astype('float32')\n",
        "  test_images = (test_images - 127.5) / 127.5\n",
        "  valid_images = train_images[50000:]\n",
        "  train_images = train_images[0:50000]\n",
        "  return train_images, valid_images, test_images\n",
        "\n",
        "train_images, _, _ = load_data()\n",
        "# Shuffle and batch the training data.\n",
        "training_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(\n",
        "    60000).batch(training_params.batch_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_vrErwKZVFCJ"
      },
      "source": [
        "## Define Architecture, Loss, Optimizers, and Utility Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NH6ymRekZyoZ"
      },
      "outputs": [],
      "source": [
        "def make_generator_model():\n",
        "  model = tf.keras.Sequential()\n",
        "  model.add(\n",
        "      layers.Dense(\n",
        "          7 * 7 * 256, use_bias=False, input_shape=(training_params.noise_dim,)\n",
        "      )\n",
        "  )\n",
        "  model.add(layers.BatchNormalization(momentum=0.9))\n",
        "  model.add(layers.ReLU())\n",
        "\n",
        "  model.add(layers.Reshape((7, 7, 256)))\n",
        "  assert model.output_shape == (None, 7, 7, 256)\n",
        "  model.add(\n",
        "      layers.Conv2DTranspose(\n",
        "          128, (5, 5), strides=(1, 1), padding='same', use_bias=False\n",
        "      )\n",
        "  )\n",
        "  assert model.output_shape == (None, 7, 7, 128)\n",
        "  model.add(layers.BatchNormalization(momentum=0.9))\n",
        "  model.add(layers.ReLU())\n",
        "  model.add(\n",
        "      layers.Conv2DTranspose(\n",
        "          64, (5, 5), strides=(2, 2), padding='same', use_bias=False\n",
        "      )\n",
        "  )\n",
        "  assert model.output_shape == (None, 14, 14, 64)\n",
        "  model.add(layers.BatchNormalization(momentum=0.9))\n",
        "  model.add(layers.ReLU())\n",
        "  model.add(\n",
        "      layers.Conv2DTranspose(\n",
        "          1,\n",
        "          (5, 5),\n",
        "          strides=(2, 2),\n",
        "          padding='same',\n",
        "          use_bias=False,\n",
        "          activation='tanh',\n",
        "      )\n",
        "  )\n",
        "  assert model.output_shape == (None, 28, 28, 1)\n",
        "  return model\n",
        "\n",
        "\n",
        "def make_discriminator_model():\n",
        "  model = tf.keras.Sequential()\n",
        "  model.add(\n",
        "      layers.Conv2D(\n",
        "          64, (5, 5), strides=(2, 2), padding='same', input_shape=[28, 28, 1]\n",
        "      )\n",
        "  )\n",
        "  model.add(layers.BatchNormalization(momentum=0.9))\n",
        "  model.add(layers.LeakyReLU())\n",
        "  model.add(layers.Dropout(0.3))\n",
        "  model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))\n",
        "  model.add(layers.BatchNormalization(momentum=0.9))\n",
        "  model.add(layers.LeakyReLU())\n",
        "  model.add(layers.Dropout(0.3))\n",
        "  model.add(layers.Flatten())\n",
        "  model.add(layers.Dense(1))\n",
        "  return model\n",
        "\n",
        "generator = make_generator_model()\n",
        "discriminator = make_discriminator_model()\n",
        "\n",
        "cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
        "\n",
        "def discriminator_loss(real_output, fake_output):\n",
        "  real_loss = cross_entropy(tf.ones_like(real_output), real_output)\n",
        "  fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)\n",
        "  total_loss = real_loss + fake_loss\n",
        "  return total_loss\n",
        "\n",
        "def generator_loss(fake_output):\n",
        "  return cross_entropy(tf.ones_like(fake_output), fake_output)\n",
        "\n",
        "\n",
        "generator_optimizer = tf.keras.optimizers.Adam(training_params.lr,\n",
        "                                               beta_1=training_params.beta_1)\n",
        "discriminator_optimizer = tf.keras.optimizers.Adam(training_params.lr,\n",
        "                                                  beta_1=training_params.beta_1)\n",
        "\n",
        "# Utility function for visualizing images during training.\n",
        "def visualize_images(model, test_input):\n",
        "  predictions = model(test_input, training=False)\n",
        "  fig = plt.figure(figsize=(8, 8))\n",
        "  for i in range(predictions.shape[0]):\n",
        "      plt.subplot(4, 4, i+1)\n",
        "      plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')\n",
        "      plt.axis('off')\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cqqsH08fVh8I"
      },
      "source": [
        "## Training Loop\n",
        "\n",
        "We train the discriminator and generator simultaneously using SGD. The LR scheduler is invoked inside the `train_step` function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "height": 1000
        },
        "executionInfo": {
          "elapsed": 167301,
          "status": "ok",
          "timestamp": 1674598365152,
          "user": {
            "displayName": "Hussein Hazimeh",
            "userId": "12699877532746750614"
          },
          "user_tz": 300
        },
        "id": "tG1jbqnKfayJ",
        "outputId": "c489c376-25cc-44a5-d96b-af20ee4485a2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch:  1\n"
          ]
        },
        {
          "data": {
            "image/png": 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j77gM77lZcxSfuzxHsT5Dhw7NyjDqkZqjaosaRsjBSD9qe5HapoW/W0zUqpr1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\ntVKI5Qn8lMWguBw9BSXPsWPHZmW4ErIeuYE/r3/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H+01FkWKqbnNCfw2X4b1Nnjy54X3a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\njTHGGIFflMYYY4zg/wOEGdeXutWSyAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cFigure size 800x800 with 16 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch:  10\n"
          ]
        },
        {
          "data": {
            "image/png": 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Yk2cmT6KoM4bXYJjWXnvtFfRDU+nkyZNV33XXXUE/NK8PGjQoaENTeJSrJCvw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\nXr/cmnMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cFigure size 800x800 with 16 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch:  20\n"
          ]
        },
        {
          "data": {
            "image/png": 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9Wddff73F5513nldPp3qFrrnmGovHjBljsfZFZ1sx3KPhTkp6v8V2adElQnWp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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cFigure size 800x800 with 16 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch:  30\n"
          ]
        },
        {
          "data": {
            "image/png": 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PGjXK4hYtWnhtWtmlW7duFmu61jnn2rZta3HSS3yKYePmk08+2Xutww9hZRW9P3Tzg/Bz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\n",
            "text/plain": [
              "\u003cFigure size 800x800 with 16 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch:  40\n"
          ]
        },
        {
          "data": {
            "image/png": 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9EEIYO3asxTp34LvvvouO02UfujwshLhPKfYOP8XGEyUAAAl0lAAAJOSdevWP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\n",
            "text/plain": [
              "\u003cFigure size 800x800 with 16 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch:  50\n"
          ]
        },
        {
          "data": {
            "image/png": 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FWi7ys88+y/i7fKk7vaZ0WZ4v+1gO1x53lAAAJNBRAgCQkHXqtSa7IJTqVnmr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\nQAIdJQAACXSUAAAk/D8IlH0878vKmwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cFigure size 800x800 with 16 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "@tf.function\n",
        "def train_step(real_images, smoothed_disc_loss):\n",
        "  \"\"\"Training step for G and D.\n",
        "\n",
        "  Runs SGD to simultaneously update G and D. The LR scheduler is invoked inside\n",
        "  this function. `smoothed_disc_loss` is an exponential moving average of D's\n",
        "  loss, which is used by the scheduler (as an estimate of D's loss over the\n",
        "  whole training set). `smoothed_disc_loss` is updated and returned by this\n",
        "  function after processing the input batch (real_images).\n",
        "\n",
        "  Args:\n",
        "    real_images: a batch of real images.\n",
        "    smoothed_disc_loss: current exponential moving average of D's loss.\n",
        "\n",
        "  Returns:\n",
        "    A scalar representing the updated value of smoothed_disc_loss after\n",
        "      processing the latest training batch.\n",
        "  \"\"\"\n",
        "\n",
        "  noise = tf.random.normal([training_params.batch_size,\n",
        "                            training_params.noise_dim])\n",
        "\n",
        "  with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
        "    generated_images = generator(noise, training=True)\n",
        "\n",
        "    real_output = discriminator(real_images, training=True)\n",
        "    fake_output = discriminator(generated_images, training=True)\n",
        "\n",
        "    gen_loss = generator_loss(fake_output)\n",
        "    disc_loss = discriminator_loss(real_output, fake_output)\n",
        "\n",
        "  gradients_of_generator = gen_tape.gradient(gen_loss,\n",
        "                                              generator.trainable_variables)\n",
        "  gradients_of_discriminator = disc_tape.gradient(\n",
        "      disc_loss, discriminator.trainable_variables)\n",
        "\n",
        "  generator_optimizer.apply_gradients(\n",
        "      zip(gradients_of_generator, generator.trainable_variables))\n",
        "\n",
        "  if training_params.use_lr_sched:\n",
        "    # Update the exponential moving average.\n",
        "    smoothed_disc_loss = 0.95 * smoothed_disc_loss + 0.05 * disc_loss\n",
        "    # Call the scheduler and update the LR of D.\n",
        "    lr = training_params.lr * lr_scheduler(smoothed_disc_loss,\n",
        "                                scheduler_params.ideal_loss,\n",
        "                                scheduler_params.x_min,\n",
        "                                scheduler_params.x_max,\n",
        "                                scheduler_params.h_min,\n",
        "                                scheduler_params.f_max)\n",
        "    discriminator_optimizer.lr.assign(lr)\n",
        "\n",
        "  discriminator_optimizer.apply_gradients(\n",
        "      zip(gradients_of_discriminator, discriminator.trainable_variables))\n",
        "\n",
        "  return smoothed_disc_loss\n",
        "\n",
        "\n",
        "noise_for_visualization = np.random.normal(size=(16,training_params.noise_dim))\n",
        "\n",
        "# Initialize smoothed_disc_loss to be equal to ideal_loss.\n",
        "smoothed_disc_loss = scheduler_params.ideal_loss\n",
        "\n",
        "# Training loop.\n",
        "for epoch in range(training_params.num_epochs):\n",
        "  for image_batch in training_dataset:\n",
        "    smoothed_disc_loss = train_step(image_batch, smoothed_disc_loss)\n",
        "\n",
        "  # Visualize every 10 epochs.\n",
        "  if epoch == 0 or (epoch+1) % 10 == 0:\n",
        "    print('Epoch: ', epoch+1)\n",
        "    visualize_images(generator, noise_for_visualization)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "jAfZuG73WlzH",
        "aVTj6D0wU3ZQ",
        "CWDUMS32UnnZ",
        "_vrErwKZVFCJ"
      ],
      "last_runtime": {
        "build_target": "//learning/deepmind/dm_python:dm_notebook3",
        "kind": "private"
      },
      "provenance": [
        {
          "file_id": "1GuGHcke3qqhP7n8O1sJmi8dhcntcwpEQ",
          "timestamp": 1674600322404
        },
        {
          "file_id": "1OEsIzdoufXu85XjbSVV6sG0XsZcZWrib",
          "timestamp": 1674583601935
        }
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
